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Related Experiment Video

Updated: Feb 7, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

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Published on: June 18, 2020

Semi-Supervised Metric Learning-Based Anchor Graph Hashing for Large-Scale Image Retrieval.

Haifeng Hu, Kun Wang, Chenggang Lv

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 7, 2018

Summary

This study introduces a novel semi-supervised anchor graph hashing method for efficient image retrieval. The approach preserves semantic similarity and data structure, outperforming existing techniques on benchmarks.

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Area of Science:

Computer Science
Information Retrieval
Machine Learning

Background:

Hashing-based image retrieval offers high efficiency and low cost.
Existing methods face challenges in preserving semantic similarity and data structure during hash learning.

Purpose of the Study:

To propose a semi-supervised metric learning-based anchor graph hashing method.
To efficiently learn hash codes that maintain semantic similarity and data structure.

Main Methods:

Constructing an anchor-based similarity graph using a transformation matrix.
Developing an objective function based on triplet relationships with label smoothness and margin hinge loss.
Utilizing stochastic gradient descent (SGD) with a penalty factor for efficient matrix updates.

Main Results:

The proposed method demonstrates feasibility and advantages over state-of-the-art techniques.
Experimental validation on image benchmarks confirms superior retrieval performance.

Conclusions:

The semi-supervised anchor graph hashing method effectively balances semantic and structural information.
This approach enhances the efficiency and accuracy of hashing-based image retrieval.